Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy.

Approximately 60% of patients with large B cell lymphoma treated with chimeric antigen receptor (CAR) T cell therapies targeting CD19 experience disease progression, and neurotoxicity remains a challenge. Biomarkers associated with resistance and toxicity are limited. In this study, single-cell proteomic profiling of circulating CAR T cells in 32 patients treated with CD19-CAR identified that CD4+Helios+ CAR T cells on day 7 after infusion are associated with progressive disease and less severe neurotoxicity. Deep profiling demonstrated that this population is non-clonal and manifests hallmark features of T regulatory (TReg) cells. Validation cohort analysis upheld the link between higher CAR TReg cells with clinical progression and less severe neurotoxicity. A model combining expansion of this subset with lactate dehydrogenase levels, as a surrogate for tumor burden, was superior for predicting durable clinical response compared to models relying on each feature alone. These data credential CAR TReg cell expansion as a novel biomarker of response and toxicity after CAR T cell therapy and raise the prospect that this subset may regulate CAR T cell responses in humans.

Testing for a Sweet Spot in Randomized Trials.

INTRODUCTION: Randomized trials recruit diverse patients, including some individuals who may be unresponsive to the treatment. Here we follow up on prior conceptual advances and introduce a specific method that does not rely on stratification analysis and that tests whether patients in the intermediate range of disease severity experience more relative benefit than patients at the extremes of disease severity (sweet spot). METHODS: We contrast linear models to sigmoidal models when describing associations between disease severity and accumulating treatment benefit. The Gompertz curve is highlighted as a specific sigmoidal curve along with the Akaike information criterion (AIC) as a measure of goodness of fit. This approach is then applied to a matched analysis of a published landmark randomized trial evaluating whether implantable defibrillators reduce overall mortality in cardiac patients (n = 2,521). RESULTS: The linear model suggested a significant survival advantage across the spectrum of increasing disease severity (ß = 0.0847, P < 0.001, AIC = 2,491). Similarly, the sigmoidal model suggested a significant survival advantage across the spectrum of disease severity (α = 93, ß = 4.939, γ = 0.00316, P < 0.001 for all, AIC = 1,660). The discrepancy between the 2 models indicated worse goodness of fit with a linear model compared to a sigmoidal model (AIC: 2,491 v. 1,660, P < 0.001), thereby suggesting a sweet spot in the midrange of disease severity. Model cross-validation using computational statistics also confirmed the superior goodness of fit of the sigmoidal curve with a concentration of survival benefits for patients in the midrange of disease severity. CONCLUSION: Systematic methods are available beyond simple stratification for identifying a sweet spot according to disease severity. The approach can assess whether some patients experience more relative benefit than other patients in a randomized trial.[Box: see text].

An approach to explore for a sweet spot in randomized trials.

OBJECTIVE: The objective of the study was to demonstrate how a conventional randomized trial can be analyzed through a stratified or a matched approach to identify a potential sweet spot where observed differences might be accentuated in the mid range of disease severity. DESIGN AND SETTING: We review a landmark randomized trial of heart failure patients that tested whether implantable defibrillators reduce mortality (n = 2,521). RESULTS: Overall, 22% (182/829) of the patients in the defibrillator group died compared with 29% (484/1,692) of patients in the control group. Proportional hazards analysis yielded a modest 25% survival benefit (hazard ratio = 0.75, 95% confidence interval: 0.63 to 0.89). Stratified analysis of the trial yielded a larger 52% survival benefit for those in the middle quintile of disease severity (hazard ratio = 0.48, 95% confidence interval: 0.29 to 0.79). In contrast, little of the survival benefit was explained by patients with the greatest disease severity (hazard ratio = 0.89, 95% confidence interval: 0.69 to 1.15). The discrepancy between crude and stratified analyses could be visualized by graphical displays and replicated with matched comparisons. CONCLUSION: Our approach for analyzing a randomized trial could help identify a potential sweet spot of an accentuated treatment effect.

Increased T Cell Differentiation and Cytolytic Function in Bangladeshi Compared to American Children.

During the first 5 years of life, children are especially vulnerable to infection-related morbidity and mortality. Conversely, the Hygiene Hypothesis suggests that a lack of exposure to infectious agents early in life could explain the increasing incidence of allergies and autoimmunity in high-income countries. Understanding these phenomena, however, is hampered by a lack of comprehensive, direct immune monitoring in children with differing degrees of microbial exposure. Using mass cytometry, we provide an in-depth profile of the peripheral blood mononuclear cells (PBMCs) of children in regions at the extremes of exposure: the San Francisco Bay Area, USA and an economically poor district of Dhaka, Bangladesh. Despite variability in clinical health, functional characteristics of PBMCs were similar in Bangladeshi and American children at 1 year of age. However, by 2-3 years of age, Bangladeshi children's immune cells often demonstrated altered activation and cytokine production profiles upon stimulation with PMA-ionomycin, with an overall immune trajectory more in line with American adults. Conversely, immune responses in children from the US remained steady. Using principal component analysis, donor location, ethnic background, and cytomegalovirus infection status were found to account for some of the variation identified among samples. Within Bangladeshi 1-year-olds, stunting (as measured by height-for-age z-scores) was found to be associated with IL-8 and TGFß expression in PMA-ionomycin stimulated samples. Combined, these findings provide important insights into the immune systems of children in high vs. low microbial exposure environments and suggest an important role for IL-8 and TGFß in mitigating the microbial challenges faced by the Bangladeshi children.

Early detection of unilateral ureteral obstruction by desorption electrospray ionization mass spectrometry.

Desorption electrospray ionization mass spectrometry (DESI-MS) is an emerging analytical tool for rapid in situ assessment of metabolomic profiles on tissue sections without tissue pretreatment or labeling. We applied DESI-MS to identify candidate metabolic biomarkers associated with kidney injury at the early stage. DESI-MS was performed on sections of kidneys from 80 mice over a time course following unilateral ureteral obstruction (UUO) and compared to sham controls. A predictive model of renal damage was constructed using the LASSO (least absolute shrinkage and selection operator) method. Levels of lipid and small metabolites were significantly altered and glycerophospholipids comprised a significant fraction of altered species. These changes correlate with altered expression of lipid metabolic genes, with most genes showing decreased expression. However, rapid upregulation of PG(22:6/22:6) level appeared to be a hitherto unknown feature of the metabolic shift observed in UUO. Using LASSO and SAM (significance analysis of microarrays), we identified a set of well-measured metabolites that accurately predicted UUO-induced renal damage that was detectable by 12 h after UUO, prior to apparent histological changes. Thus, DESI-MS could serve as a useful adjunct to histology in identifying renal damage and demonstrates early and broad changes in membrane associated lipids.

Found In Translation: a machine learning model for mouse-to-human inference.

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.

Distinguishing malignant from benign microscopic skin lesions using desorption electrospray ionization mass spectrometry imaging.

Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m/z 200-1,200 and Krebs cycle metabolites observed at m/z < 200. Based on these features, Lasso yielded an overall 94.1% diagnostic accuracy pixel by pixel of the skin map compared with histopathological evaluation. We suggest that DESI-MSI/Lasso analysis can be employed as a complementary technique for delineation of microscopic skin tumors.

Methods for analyzing matched designs with double controls: excess risk is easily estimated and misinterpreted when evaluating traffic deaths.

OBJECTIVES: To demonstrate analytic approaches for matched studies where two controls are linked to each case and events are accumulating counts rather than binary outcomes. A secondary intent is to clarify the distinction between total risk and excess risk (unmatched vs. matched perspectives). STUDY DESIGN AND SETTING: We review past research testing whether elections can lead to increased traffic risks. The results are reinterpreted by analyzing both the total count of individuals in fatal crashes and the excess count of individuals in fatal crashes, each time accounting for the matched double controls. RESULTS: Overall, 1,546 individuals were in fatal crashes on the 10 election days (average = 155/d), and 2,593 individuals were in fatal crashes on the 20 control days (average = 130/d). Poisson regression of total counts yielded a relative risk of 1.19 (95% confidence interval: 1.12-1.27). Poisson regression of excess counts yielded a relative risk of 3.22 (95% confidence interval: 2.72-3.80). The discrepancy between analyses of total counts and excess counts replicated with alternative statistical models and was visualized in graphical displays. CONCLUSION: Available approaches provide methods for analyzing count data in matched designs with double controls and help clarify the distinction between increases in total risk and increases in excess risk.

CFH and ARMS2 genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation.

We evaluated the influence of an antioxidant and zinc nutritional supplement [the Age-Related Eye Disease Study (AREDS) formulation] on delaying or preventing progression to neovascular AMD (NV) in persons with age-related macular degeneration (AMD). AREDS subjects (n = 802) with category 3 or 4 AMD at baseline who had been treated with placebo or the AREDS formulation were evaluated for differences in the risk of progression to NV as a function of complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) genotype groups. We used published genetic grouping: a two-SNP haplotype risk-calling algorithm to assess CFH, and either the single SNP rs10490924 or 372_815del443ins54 to mark ARMS2 risk. Progression risk was determined using the Cox proportional hazard model. Genetics-treatment interaction on NV risk was assessed using a multiiterative bootstrap validation analysis. We identified strong interaction of genetics with AREDS formulation treatment on the development of NV. Individuals with high CFH and no ARMS2 risk alleles and taking the AREDS formulation had increased progression to NV compared with placebo. Those with low CFH risk and high ARMS2 risk had decreased progression risk. Analysis of CFH and ARMS2 genotype groups from a validation dataset reinforces this conclusion. Bootstrapping analysis confirms the presence of a genetics-treatment interaction and suggests that individual treatment response to the AREDS formulation is largely determined by genetics. The AREDS formulation modifies the risk of progression to NV based on individual genetics. Its use should be based on patient-specific genotype.

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system.

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.

Diagnosis of prostate cancer by desorption electrospray ionization mass spectrometric imaging of small metabolites and lipids.

Accurate identification of prostate cancer in frozen sections at the time of surgery can be challenging, limiting the surgeon's ability to best determine resection margins during prostatectomy. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on 54 banked human cancerous and normal prostate tissue specimens to investigate the spatial distribution of a wide variety of small metabolites, carbohydrates, and lipids. In contrast to several previous studies, our method included Krebs cycle intermediates (m/z <200), which we found to be highly informative in distinguishing cancer from benign tissue. Malignant prostate cells showed marked metabolic derangements compared with their benign counterparts. Using the "Least absolute shrinkage and selection operator" (Lasso), we analyzed all metabolites from the DESI-MS data and identified parsimonious sets of metabolic profiles for distinguishing between cancer and normal tissue. In an independent set of samples, we could use these models to classify prostate cancer from benign specimens with nearly 90% accuracy per patient. Based on previous work in prostate cancer showing that glucose levels are high while citrate is low, we found that measurement of the glucose/citrate ion signal ratio accurately predicted cancer when this ratio exceeds 1.0 and normal prostate when the ratio is less than 0.5. After brief tissue preparation, the glucose/citrate ratio can be recorded on a tissue sample in 1 min or less, which is in sharp contrast to the 20 min or more required by histopathological examination of frozen tissue specimens.

A simple method for analyzing matched designs with double controls: McNemar's test can be extended.

OBJECTIVES: To introduce a new analytic approach for matched studies, where exactly two controls are linked to each case (double controls rather than solitary controls). The intent is to extend McNemar's test for one-to-two matching (instead of one-to-one matching) when evaluating binary predictors and outcomes. STUDY DESIGN AND SETTING: We review McNemar's approach for analyzing matched data, demonstrate the Mantel-Haenszel approach for integrating two overlapping McNemar's estimates, review conditional logistic regression as an alternative analytic approach, and introduce a new method that yields a visual display and easy verification. RESULTS: We illustrate the new approach with real data testing the association between overcast weather and the risk of a life-threatening traffic crash (n = 6,962). We show that results from the new approach agree closely with conditional logistic regression and are sufficiently simple as to be computed on a handheld calculator. We further validate the approach by conducting simulations when a positive association was predefined and when a null association was predefined. CONCLUSION: The new approach provides a feasible, simple, and efficient method for analyzing matched designs with double controls.

High-dimensional regression adjustments in randomized experiments.

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.

Successful immunotherapy induces previously unidentified allergen-specific CD4+ T-cell subsets.

Allergen immunotherapy can desensitize even subjects with potentially lethal allergies, but the changes induced in T cells that underpin successful immunotherapy remain poorly understood. In a cohort of peanut-allergic participants, we used allergen-specific T-cell sorting and single-cell gene expression to trace the transcriptional "roadmap" of individual CD4+ T cells throughout immunotherapy. We found that successful immunotherapy induces allergen-specific CD4+ T cells to expand and shift toward an "anergic" Th2 T-cell phenotype largely absent in both pretreatment participants and healthy controls. These findings show that sustained success, even after immunotherapy is withdrawn, is associated with the induction, expansion, and maintenance of immunotherapy-specific memory and naive T-cell phenotypes as early as 3 mo into immunotherapy. These results suggest an approach for immune monitoring participants undergoing immunotherapy to predict the success of future treatment and could have implications for immunotherapy targets in other diseases like cancer, autoimmune disease, and transplantation.

The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma.

PURPOSE: To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image. MATERIALS AND METHODS: In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis. RESULTS: Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001). CONCLUSION: A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.

Statistical learning and selective inference.

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Fibromyalgia and the Risk of a Subsequent Motor Vehicle Crash.

OBJECTIVE: Motor vehicle crashes are a widespread contributor to mortality and morbidity, sometimes related to medically unfit motorists. We tested whether patients diagnosed with fibromyalgia (FM) have an increased risk of a subsequent serious motor vehicle crash. METHODS: We conducted a population-based self-matched longitudinal cohort analysis to estimate the incidence rate ratio of crashes among patients diagnosed with FM relative to the population norm in Ontario, Canada. We included adults diagnosed from April 1, 2006, to March 31, 2012, excluding individuals younger than 18 years, living outside Ontario, lacking valid identifiers, or having only a single visit for the diagnosis. The primary outcome was an emergency department visit as a driver involved in a motor vehicle crash. RESULTS: The patients (n = 137,631) accounted for 738 crashes during the first year of followup after diagnosis, equal to an incidence rate ratio of 2.44 compared with the population norm (95% CI 2.27-2.63, p < 0.001). The crash rate was more than twice the population norm for those with a new or a persistent diagnosis. The increased risk included patients with diverse characteristics, approached the rate observed among other patients diagnosed with alcoholism, and was mitigated among those who received dedicated FM care or a physician warning for driving safety. CONCLUSION: A diagnosis of FM is associated with an increased risk of a subsequent motor vehicle crash that might justify medical interventions for traffic safety.

Automated identification of stratifying signatures in cellular subpopulations.

Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry increase the parameterization of single-cell measurements, the scalability and subjectivity inherent in manual analyses slows both analysis and progress. We therefore developed Citrus (cluster identification, characterization, and regression), a data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. The methodology of Citrus is demonstrated through the identification of known and unexpected pathway responses in a dataset of stimulated peripheral blood mononuclear cells measured by mass cytometry. Additionally, the performance of Citrus is compared with that of existing methods through the analysis of several publicly available datasets. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will be needed to aid investigators in the performance of unbiased--and potentially more thorough--correlation-based mining and inspection of cell subsets nested within high-dimensional datasets.